
R version 3.6.0 (2019-04-26) -- "Planting of a Tree"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

[Previously saved workspace restored]

> # Run QAP -- mentions 
> rm(list = ls())
> 
> library(sna)
Loading required package: statnet.common

Attaching package: 'statnet.common'

The following object is masked from 'package:base':

    order

Loading required package: network
network: Classes for Relational Data
Version 1.16.1 created on 2020-10-06.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
                    Mark S. Handcock, University of California -- Los Angeles
                    David R. Hunter, Penn State University
                    Martina Morris, University of Washington
                    Skye Bender-deMoll, University of Washington
 For citation information, type citation("network").
 Type help("network-package") to get started.

sna: Tools for Social Network Analysis
Version 2.6 created on 2020-10-5.
copyright (c) 2005, Carter T. Butts, University of California-Irvine
 For citation information, type citation("sna").
 Type help(package="sna") to get started.

> library(doParallel)
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
> library(doRNG)
Loading required package: rngtools
> 
> 
> # Mentions adjacency matrix
> y <- readRDS("processed_data/mentions_adjacencyMatrix.Rds")
> 
> # load similarity matrix(s)
> state_similarity_matrix <- readRDS("processed_data/state_similarity_matrix.Rds")
> party_similarity_matrix <- readRDS("processed_data/party_similarity_matrix.Rds")
> chamber_similarity_matrix <- readRDS("processed_data/chamber_similarity_matrix.Rds")
> gender_similarity_matrix <- readRDS("processed_data/gender_similarity_matrix.Rds")
> race_similarity_matrix <- readRDS("processed_data/same_race.Rds")
> profesh_diff_matrix <- readRDS("processed_data/profeshScore_diff.Rds")
> 
> # Sender matrix
> democrat_xs <- readRDS("processed_data/democrat_sender_effect.Rds")
> republican_xs <- readRDS("processed_data/republican_sender_effect.Rds")
> house_xs <- readRDS("processed_data/house_sender_matrix.Rds")
> gender_xs <- readRDS("processed_data/gender_sender_matrix.Rds")
> profesh_xs <- readRDS("processed_data/sender_profesh.Rds")
> black_xs <- readRDS("processed_data/sender_black.Rds")
> latino_xs <- readRDS("processed_data/sender_latino.Rds")
> asian_xs <- readRDS("processed_data/sender_asian.Rds")
> mena_xs <- readRDS("processed_data/sender_mena.Rds")
> multi_xs <- readRDS("processed_data/sender_multi.Rds")
> native_xs <- readRDS("processed_data/sender_native.Rds")
> 
> # Receiver effects
> democrat_xr <- readRDS("processed_data/democrat_receiver_effect.Rds")
> republican_xr <- readRDS("processed_data/republican_receiver_effect.Rds")
> house_xr <- readRDS("processed_data/house_receiver_matrix.Rds")
> gender_xr <- readRDS("processed_data/gender_receiver_matrix.Rds")
> profesh_xr <- readRDS("processed_data/receiver_profesh.Rds")
> black_xr <- readRDS("processed_data/receiver_black.Rds")
> latino_xr <- readRDS("processed_data/receiver_latino.Rds")
> asian_xr <- readRDS("processed_data/receiver_asian.Rds")
> mena_xr <- readRDS("processed_data/receiver_mena.Rds")
> multi_xr <- readRDS("processed_data/receiver_multi.Rds")
> native_xr <- readRDS("processed_data/receiver_native.Rds")
> 
> # Interaction effects
> party_sameState_interaction_matrix <- readRDS("processed_data/party_sameState_interaction_matrix.Rds")
> chamber_sameState_interaction_matrix <- readRDS("processed_data/chamber_sameState_interaction_matrix.Rds")
> gender_sameState_interaction_matrix <- readRDS("processed_data/gender_sameState_interaction_matrix.Rds")
> race_sameState_interaction_matrix <- readRDS("processed_data/race_sameState_interaction_matrix.Rds")
> 
> 
> # Contiguos  states
> contiguous <- readRDS("processed_data/contig_states_matrix.Rds")
> 
> # join the predicting matrices together
> predicting_matrices <- array (NA, c(33, 
+                                     length(state_similarity_matrix[1,]), 
+                                     length(state_similarity_matrix[1,])))
> 
> # Similarity
> predicting_matrices[1,,] <- state_similarity_matrix
> rm(state_similarity_matrix)
> 
> predicting_matrices[2,,] <- party_similarity_matrix
> rm(party_similarity_matrix)
> 
> predicting_matrices[3,,] <- chamber_similarity_matrix
> rm(chamber_similarity_matrix)
> 
> predicting_matrices[4,,] <- gender_similarity_matrix
> rm(gender_similarity_matrix)
> 
> predicting_matrices[5,,] <- race_similarity_matrix
> rm(race_similarity_matrix)
> 
> predicting_matrices[6,,] <- profesh_diff_matrix
> rm(profesh_diff_matrix)
> 
> 
> # Sender Effects
> predicting_matrices[7,,] <- democrat_xs
> rm(democrat_xs)
> 
> predicting_matrices[8,,] <- republican_xs
> rm(republican_xs)
> 
> predicting_matrices[9,,] <- house_xs
> rm(house_xs)
> 
> predicting_matrices[10,,] <- gender_xs
> rm(gender_xs)
> 
> predicting_matrices[11,,] <- profesh_xs
> rm(profesh_xs)
> 
> predicting_matrices[12,,] <- black_xs
> rm(black_xs)
> 
> predicting_matrices[13,,] <- latino_xs
> rm(latino_xs)
> 
> predicting_matrices[14,,] <- asian_xs
> rm(asian_xs)
> 
> predicting_matrices[15,,] <- mena_xs
> rm(mena_xs)
> 
> predicting_matrices[16,,] <- multi_xs
> rm(multi_xs)
> 
> predicting_matrices[17,,] <- native_xs
> rm(native_xs)
> 
> 
> # Receiver Effects
> predicting_matrices[18,,] <- democrat_xr
> rm(democrat_xr)
> 
> predicting_matrices[19,,] <- republican_xr
> rm(republican_xr)
> 
> predicting_matrices[20,,] <- house_xr
> rm(house_xr)
> 
> predicting_matrices[21,,] <- gender_xr
> rm(gender_xr)
> 
> predicting_matrices[22,,] <- profesh_xr
> rm(profesh_xr)
> 
> predicting_matrices[23,,] <- black_xr
> rm(black_xr)
> 
> predicting_matrices[24,,] <- latino_xr
> rm(latino_xr)
> 
> predicting_matrices[25,,] <- asian_xr
> rm(asian_xr)
> 
> predicting_matrices[26,,] <- mena_xr
> rm(mena_xr)
> 
> predicting_matrices[27,,] <- multi_xr
> rm(multi_xr)
> 
> predicting_matrices[28,,] <- native_xr
> rm(native_xr)
> 
> # Interaction Effects
> predicting_matrices[29,,] <- party_sameState_interaction_matrix
> rm(party_sameState_interaction_matrix)
> 
> predicting_matrices[30,,] <- chamber_sameState_interaction_matrix
> rm(chamber_sameState_interaction_matrix)
> 
> predicting_matrices[31,,] <- gender_sameState_interaction_matrix
> rm(gender_sameState_interaction_matrix)
> 
> predicting_matrices[32,,] <- race_sameState_interaction_matrix
> rm(race_sameState_interaction_matrix)
> 
> # Contiguous States 
> predicting_matrices[33,,] <- contiguous
> rm(contiguous)
> 
> # run QAP in Parallel
> #cl <- makeCluster(2, outfile="")  #11
> #registerDoParallel(cl)
> 
> #set.seed(10)
> #system.time(qap_res <- foreach(i=1:2,.packages=c("sna", "doRNG")) %dorng% {
> #  netlm(y, predicting_matrices, reps=2)
> #})
> 
> #stopCluster(cl)
> 
> start.time <- Sys.time()
> # Set seed value 
> seed_value <- 17
> set.seed(seed_value)
> # Run netlm
> qap_res <- netlm(y, predicting_matrices, reps = 25)
> end.time <- Sys.time()
> time.taken <- end.time - start.time
> time.taken
Time difference of 1.372991 days
> 
> # Save results 
> file_name <- paste0("output/qap_res_mentions_out", seed_value, ".Rds")
> saveRDS(qap_res, file=file_name)
> 
> 
> 
> 
> proc.time()
     user    system   elapsed 
 91864.67  26925.67 118816.65 
